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Predictive Soil Mapping with R

Tomislav Hengl; Robert A. MacMillan

Predictive Soil Mapping (PSM) is based on applying statistical and/or machine learning techniques to fit models for the purpose of producing spatial and/or spatiotemporal predictions of soil variables i.e. maps of soil properties and classes at different resolutions. It is a multidisciplinary field combining statistics, data science, soil science, physical geography, remote sensing, geoinformation science and number of other sciences. Predictive Soil Mapping with R focuses on using state of the art Statistical and Machine Learning techniques to produce more accurate and more usable maps of soil variables.

Follow the book progress via the book's github repository. The html version of the book is available at https://envirometrix.github.io/PredictiveSoilMapping/.

Cite this as:

  • Hengl, T., MacMillan, R.A., (2019). Predictive Soil Mapping with R. OpenGeoHub foundation, Wageningen, the Netherlands, 370 pages, www.soilmapper.org, ISBN: 978-0-359-30635-0.

ISBN: 978-0-359-30635-0
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